Optimizing the maximum reported cluster size for the multinomial-based spatial scan statistic.

ABSTRACT:

Background

Correctly identifying spatial disease cluster is a fundamental concern in public health and epidemiology. The spatial scan statistic is widely used for detecting spatial disease clusters in spatial epidemiology and disease surveillance. Many studies default to a maximum reported cluster size (MRCS) set at 50% of the total population when searching for spatial clusters. However, this default setting can sometimes report clusters larger than true clusters, which include less relevant regions. For the Poisson, Bernoulli, ordinal, normal, and exponential models, a Gini coefficient has been developed to optimize the MRCS. Yet, no measure is available for the multinomial model.

Results

We propose two versions of a spatial cluster information criterion (SCIC) for selecti

SUBMITTER: Moon J 

PROVIDER: S-EPMC10631089 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature